Hacettepe University Departmant

of Computer Engineering

BBM 415 Fundamentals of Image

Processing Laboratory

Fall 2022-2023

Programming Assignment 1 Report

Omer Kaan Vural

21987537

03.11.2022

Images of the Russian Empire: Colorizing

the Prokudin-Gorskii Photo Collection

Overview

The images obtained by a Russian photographer by investing the photos he once took

with red, green and blue filters are presented digitally in grayscale format. In this

assignment, we will try to combine and colorize these submitted photos. We will also make

various improvements using some algorithms.

Details of Approach

- With the opencv library, the methods of reading pictures and making changes in

pixels thanks to matrices were comprehended.

- Frames in the picture removed by cutting a certain percentage of the picture

- A single image file contains information of 3 different color channels. So by dividing

the picture into 3 equal parts, I brought it to a state where we can combine the

channels. Example below:

- I used gaussian blur for each channel to reduce noise in pictures

- After dividing the pictures into three and blurring them, we obtained a score by

exhaustive search with the NCC method in order to best match the three pictures

to each other. We aligned the pictures, identifying the best situation.

- After the best alignment was set i combined the r, g, b channels and got a colorized

picture

- After all these processes, I used various algorithms to make the pictures better and

to experiment with them. These are:

 Gamma Correction

 Laplacian Filtering

 Histogram Equalization

The results at the end of all these steps are below.

1 -> not aligned image

2 -> gaussian blurred image

3 -> blurred and aligned image

4 -> plus laplacian filtered image

5 -> plus gamma adjusted image (most of the images are bright. So 0.8 gamma is applied

all)

Ordering as -> 1 2

3 4

Alignment Problem

Most of the images can not be splitted so well. Because border degrees and flatness are

different. So exhaustive search is applied with wide range (-40, 40).

Also, if I use laplacian filter, aligning red and green channels by blue does not work well.

So, the best alignment show up alignin by green channel when use laplacian. I think this

problem might be occurred because of that I use laplacian filter raw grayscale image but

when I tried to filter channels seperately, nothing changed.

Further aligning channels differently does not make a difference except location of image.

So, I have not figured out why laplacian filter works well only aligning channels by green.

This is the reason images that laplacian filtered has not the same alignment 1., 2. and 3.

Image. Slightly difference by location of image.

Enhancement

I tested histogram equalization and laplacian filter a few images (not all). Laplacian filter

gives better results. So I do not use histogram equalization instead laplacian. All of the

images below are laplacian filtered

Image 1 (00125v.jpg)

Image 2 (00194v.jpg)

Image 3 (00137v.jpg)

Image 4 (00153v.jpg)

Image 5 (00398v.jpg)

Image 6 (00458v.jpg)

Image 7 (00804v.jpg)

Image 8 (00911v.jpg)

Image 9 (01522v.jpg)